
## Welcome to sktime

<a href="https://www.sktime.net"><img src="https://github.com/sktime/sktime/blob/main/docs/source/images/sktime-logo.svg" width="175" align="right" /></a>

> A unified interface for machine learning with time series

:rocket: **Version 0.38.5 out now!** [Check out the release notes here](https://www.sktime.net/en/latest/changelog.html).

sktime is a library for time series analysis in Python. It provides a unified interface for multiple time series learning tasks. Currently, this includes forecasting, time series classification, clustering, anomaly/changepoint detection, and other tasks. It comes with [time series algorithms](https://www.sktime.net/en/stable/estimator_overview.html) and [scikit-learn] compatible tools to build, tune, and validate time series models.

[scikit-learn]: https://scikit-learn.org/stable/

|  | **[Documentation](https://www.sktime.net/en/stable/users.html)** · **[Tutorials](https://www.sktime.net/en/stable/examples.html)** · **[Release Notes](https://www.sktime.net/en/stable/changelog.html)** |
|---|---|
| **Open&#160;Source** | [![BSD 3-clause](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](https://github.com/sktime/sktime/blob/main/LICENSE) [![GC.OS Sponsored](https://img.shields.io/badge/GC.OS-Sponsored%20Project-orange.svg?style=flat&colorA=0eac92&colorB=2077b4)](https://gc-os-ai.github.io/) |
| **Tutorials** | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/sktime/sktime/main?filepath=examples) [![!youtube](https://img.shields.io/static/v1?logo=youtube&label=YouTube&message=tutorials&color=red)](https://www.youtube.com/playlist?list=PLKs3UgGjlWHqNzu0LEOeLKvnjvvest2d0) |
| **Community** | [![!discord](https://img.shields.io/static/v1?logo=discord&label=discord&message=chat&color=lightgreen)](https://discord.com/invite/54ACzaFsn7) [![!slack](https://img.shields.io/static/v1?logo=linkedin&label=LinkedIn&message=news&color=lightblue)](https://www.linkedin.com/company/scikit-time/)  |
| **CI/CD** | [![github-actions](https://img.shields.io/github/actions/workflow/status/sktime/sktime/wheels.yml?logo=github)](https://github.com/sktime/sktime/actions/workflows/wheels.yml) [![readthedocs](https://img.shields.io/readthedocs/sktime?logo=readthedocs)](https://www.sktime.net/en/latest/?badge=latest) [![platform](https://img.shields.io/conda/pn/conda-forge/sktime)](https://github.com/sktime/sktime) |
| **Code** |  [![!pypi](https://img.shields.io/pypi/v/sktime?color=orange)](https://pypi.org/project/sktime/) [![!conda](https://img.shields.io/conda/vn/conda-forge/sktime)](https://anaconda.org/conda-forge/sktime) [![!python-versions](https://img.shields.io/pypi/pyversions/sktime)](https://www.python.org/) [![!black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)  |
| **Downloads** | ![PyPI - Downloads](https://img.shields.io/pypi/dw/sktime) ![PyPI - Downloads](https://img.shields.io/pypi/dm/sktime) [![Downloads](https://static.pepy.tech/personalized-badge/sktime?period=total&units=international_system&left_color=grey&right_color=blue&left_text=cumulative%20(pypi))](https://pepy.tech/project/sktime) |
| **Citation** | [![!zenodo](https://zenodo.org/badge/DOI/10.5281/zenodo.3749000.svg)](https://doi.org/10.5281/zenodo.3749000) |

## :books: Documentation

| Documentation                        |                                                                |
|--------------------------------------| -------------------------------------------------------------- |
| :star: **[Tutorials]**               | New to sktime? Here's everything you need to know!              |
| :clipboard: **[Binder Notebooks]**   | Example notebooks to play with in your browser.              |
| :woman_technologist: **[Examples]**  | How to use sktime and its features.                             |
| :scissors: **[Extension Templates]** | How to build your own estimator using sktime's API.            |
| :control_knobs: **[API Reference]**  | The detailed reference for sktime's API.                        |
| :tv: **[Video Tutorial]**            | Our video tutorial from 2021 PyData Global.      |
| :hammer_and_wrench: **[Changelog]**  | Changes and version history.                                   |
| :deciduous_tree: **[Roadmap]**       | sktime's software and community development plan.                                   |
| :pencil: **[Related Software]**      | A list of related software. |

[tutorials]: https://www.sktime.net/en/latest/tutorials.html
[binder notebooks]: https://mybinder.org/v2/gh/sktime/sktime/main?filepath=examples
[examples]: https://www.sktime.net/en/latest/examples.html
[video tutorial]: https://github.com/sktime/sktime-tutorial-pydata-global-2021
[api reference]: https://www.sktime.net/en/latest/api_reference.html
[changelog]: https://www.sktime.net/en/latest/changelog.html
[roadmap]: https://www.sktime.net/en/latest/roadmap.html
[related software]: https://www.sktime.net/en/latest/related_software.html

## :speech_balloon: Where to ask questions

Questions and feedback are extremely welcome! We strongly believe in the value of sharing help publicly, as it allows a wider audience to benefit from it.

| Type                            | Platforms                               |
| ------------------------------- | --------------------------------------- |
| :bug: **Bug Reports**              | [GitHub Issue Tracker]                  |
| :sparkles: **Feature Requests & Ideas** | [GitHub Issue Tracker]                       |
| :woman_technologist: **Usage Questions**          | [GitHub Discussions] · [Stack Overflow] |
| :speech_balloon: **General Discussion**        | [GitHub Discussions] |
| :factory: **Contribution & Development** | `dev-chat` channel · [Discord] |
| :globe_with_meridians: **Meet-ups and collaboration sessions** | [Discord] - Fridays 13 UTC, dev/meet-ups channel |

[github issue tracker]: https://github.com/sktime/sktime/issues
[github discussions]: https://github.com/sktime/sktime/discussions
[stack overflow]: https://stackoverflow.com/questions/tagged/sktime
[discord]: https://discord.com/invite/54ACzaFsn7

## :dizzy: Features
Our objective is to enhance the interoperability and usability of the time series analysis ecosystem in its entirety. sktime provides a __unified interface for distinct but related time series learning tasks__. It features [__dedicated time series algorithms__](https://www.sktime.net/en/stable/estimator_overview.html) and __tools for composite model building__,  such as pipelining, ensembling, tuning, and reduction, empowering users to apply algorithms designed for one task to another.

sktime also provides **interfaces to related libraries**, for example [scikit-learn], [statsmodels], [tsfresh], [PyOD], and [fbprophet], among others.

[statsmodels]: https://www.statsmodels.org/stable/index.html
[tsfresh]: https://tsfresh.readthedocs.io/en/latest/
[pyod]: https://pyod.readthedocs.io/en/latest/
[fbprophet]: https://facebook.github.io/prophet/

| Module | Status | Links |
|---|---|---|
| **[Forecasting]** | stable | [Tutorial](https://www.sktime.net/en/latest/examples/01_forecasting.html) · [API Reference](https://www.sktime.net/en/latest/api_reference/forecasting.html) · [Extension Template](https://github.com/sktime/sktime/blob/main/extension_templates/forecasting.py)  |
| **[Time Series Classification]** | stable | [Tutorial](https://github.com/sktime/sktime/blob/main/examples/02_classification.ipynb) · [API Reference](https://www.sktime.net/en/latest/api_reference/classification.html) · [Extension Template](https://github.com/sktime/sktime/blob/main/extension_templates/classification.py) |
| **[Time Series Regression]** | stable | [API Reference](https://www.sktime.net/en/latest/api_reference/regression.html) |
| **[Transformations]** | stable | [Tutorial](https://github.com/sktime/sktime/blob/main/examples/03_transformers.ipynb) · [API Reference](https://www.sktime.net/en/latest/api_reference/transformations.html) · [Extension Template](https://github.com/sktime/sktime/blob/main/extension_templates/transformer.py)  |
| **[Detection tasks]** | maturing | [Extension Template](https://github.com/sktime/sktime/blob/main/extension_templates/detection.py) |
| **[Parameter fitting]** | maturing | [API Reference](https://www.sktime.net/en/latest/api_reference/param_est.html) · [Extension Template](https://github.com/sktime/sktime/blob/main/extension_templates/transformer.py)  |
| **[Time Series Clustering]** | maturing | [API Reference](https://www.sktime.net/en/latest/api_reference/clustering.html) ·  [Extension Template](https://github.com/sktime/sktime/blob/main/extension_templates/clustering.py) |
| **[Time Series Distances/Kernels]** | maturing | [Tutorial](https://github.com/sktime/sktime/blob/main/examples/03_transformers.ipynb) · [API Reference](https://www.sktime.net/en/latest/api_reference/dists_kernels.html) · [Extension Template](https://github.com/sktime/sktime/blob/main/extension_templates/dist_kern_panel.py) |
| **[Time Series Alignment]** | experimental | [API Reference](https://www.sktime.net/en/latest/api_reference/alignment.html) · [Extension Template](https://github.com/sktime/sktime/blob/main/extension_templates/alignment.py) |
| **[Time Series Splitters]** | maturing | [Extension Template](https://github.com/sktime/sktime/blob/main/extension_templates/split.py) | |
| **[Distributions and simulation]** | experimental |  |

[forecasting]: https://github.com/sktime/sktime/tree/main/sktime/forecasting
[time series classification]: https://github.com/sktime/sktime/tree/main/sktime/classification
[time series regression]: https://github.com/sktime/sktime/tree/main/sktime/regression
[time series clustering]: https://github.com/sktime/sktime/tree/main/sktime/clustering
[detection tasks]: https://github.com/sktime/sktime/tree/main/sktime/detection
[time series distances/kernels]: https://github.com/sktime/sktime/tree/main/sktime/dists_kernels
[time series alignment]: https://github.com/sktime/sktime/tree/main/sktime/alignment
[transformations]: https://github.com/sktime/sktime/tree/main/sktime/transformations
[distributions and simulation]: https://github.com/sktime/sktime/tree/main/sktime/proba
[time series splitters]: https://github.com/sktime/sktime/tree/main/sktime/split
[parameter fitting]: https://github.com/sktime/sktime/tree/main/sktime/param_est


## :hourglass_flowing_sand: Install sktime
For troubleshooting and detailed installation instructions, see the [documentation](https://www.sktime.net/en/latest/installation.html).

- **Operating system**: macOS X · Linux · Windows 8.1 or higher
- **Python version**: Python 3.9, 3.10, 3.11, 3.12, and 3.13 (only 64-bit)
- **Package managers**: [pip] · [conda] (via `conda-forge`)

[pip]: https://pip.pypa.io/en/stable/
[conda]: https://docs.conda.io/en/latest/

### pip
Using pip, sktime releases are available as source packages and binary wheels.
Available wheels are listed [here](https://pypi.org/simple/sktime/).

```bash
pip install sktime
```

or, with maximum dependencies,

```bash
pip install sktime[all_extras]
```

For curated sets of soft dependencies for specific learning tasks:

```bash
pip install sktime[forecasting]  # for selected forecasting dependencies
pip install sktime[forecasting,transformations]  # forecasters and transformers
```

or similar. Valid sets are:

* `forecasting`
* `transformations`
* `classification`
* `regression`
* `clustering`
* `param_est`
* `networks`
* `detection`
* `alignment`

Cave: in general, not all soft dependencies for a learning task are installed,
only a curated selection.

### conda
You can also install sktime from `conda` via the `conda-forge` channel.
The feedstock including the build recipe and configuration is maintained
in [this conda-forge repository](https://github.com/conda-forge/sktime-feedstock).

```bash
conda install -c conda-forge sktime
```

or, with maximum dependencies,

```bash
conda install -c conda-forge sktime-all-extras
```

(as `conda` does not support dependency sets,
flexible choice of soft dependencies is unavailable via `conda`)

## :zap: Quickstart

### Forecasting

``` python
from sktime.datasets import load_airline
from sktime.forecasting.base import ForecastingHorizon
from sktime.forecasting.theta import ThetaForecaster
from sktime.split import temporal_train_test_split
from sktime.performance_metrics.forecasting import mean_absolute_percentage_error

y = load_airline()
y_train, y_test = temporal_train_test_split(y)
fh = ForecastingHorizon(y_test.index, is_relative=False)
forecaster = ThetaForecaster(sp=12)  # monthly seasonal periodicity
forecaster.fit(y_train)
y_pred = forecaster.predict(fh)
mean_absolute_percentage_error(y_test, y_pred)
>>> 0.08661467738190656
```

### Time Series Classification

```python
from sktime.classification.interval_based import TimeSeriesForestClassifier
from sktime.datasets import load_arrow_head
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

X, y = load_arrow_head()
X_train, X_test, y_train, y_test = train_test_split(X, y)
classifier = TimeSeriesForestClassifier()
classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)
accuracy_score(y_test, y_pred)
>>> 0.8679245283018868
```

## :wave: How to get involved

There are many ways to join the sktime community. We follow the [all-contributors](https://github.com/all-contributors/all-contributors) specification: all kinds of contributions are welcome - not just code.

| Documentation              |                                                                |
| -------------------------- | --------------------------------------------------------------        |
| :gift_heart: **[Contribute]**        | How to contribute to sktime.          |
| :school_satchel:  **[Mentoring]** | New to open source? Apply to our mentoring program! |
| :date: **[Meetings]** | Join our discussions, tutorials, workshops, and sprints! |
| :woman_mechanic:  **[Developer Guides]**      | How to further develop sktime's code base.                             |
| :construction: **[Enhancement Proposals]** | Design a new feature for sktime. |
| :medal_sports: **[Contributors]** | A list of all contributors. |
| :raising_hand: **[Roles]** | An overview of our core community roles. |
| :money_with_wings: **[Donate]** | Fund sktime maintenance and development. |
| :classical_building: **[Governance]** | How and by whom decisions are made in sktime's community.   |

[contribute]: https://www.sktime.net/en/latest/get_involved/contributing.html
[donate]: https://opencollective.com/sktime
[extension templates]: https://github.com/sktime/sktime/tree/main/extension_templates
[developer guides]: https://www.sktime.net/en/latest/developer_guide.html
[contributors]: https://github.com/sktime/sktime/blob/main/CONTRIBUTORS.md
[governance]: https://www.sktime.net/en/latest/get_involved/governance.html
[mentoring]: https://github.com/sktime/mentoring
[meetings]: https://calendar.google.com/calendar/u/0/embed?src=sktime.toolbox@gmail.com&ctz=UTC
[enhancement proposals]: https://github.com/sktime/enhancement-proposals
[roles]: https://www.sktime.net/en/latest/about/team.html

## :trophy: Hall of fame

Thanks to all our community for all your wonderful contributions, PRs, issues, ideas.

<a href="https://github.com/sktime/sktime/graphs/contributors">
<img src="https://opencollective.com/sktime/contributors.svg?width=600&button=false" />
</a>
<br>

## :bulb: Project vision

* **By the community, for the community** -- developed by a friendly and collaborative community.
* The **right tool for the right task** -- helping users to diagnose their learning problem and suitable scientific model types.
* **Embedded in state-of-art ecosystems** and **provider of interoperable interfaces** -- interoperable with [scikit-learn], [statsmodels], [tsfresh], and other community favorites.
* **Rich model composition and reduction functionality** -- build tuning and feature extraction pipelines, solve forecasting tasks with [scikit-learn] regressors.
* **Clean, descriptive specification syntax** -- based on modern object-oriented design principles for data science.
* **Fair model assessment and benchmarking** -- build your models, inspect your models, check your models, and avoid pitfalls.
* **Easily extensible** -- easy extension templates to add your own algorithms compatible with sktime's API.
